{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pydicom\n",
    "import numpy as np\n",
    "from image_processing import plt_img\n",
    "\n",
    "OUTPUT_FOLDER = 'lidc_data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = '../data/LIDC-IDRI/'  + \"LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/000040.dcm\"\n",
    "dicom = pydicom.dcmread(path)\n",
    "image = pydicom.dcmread(path).pixel_array\n",
    "# plt_img(image)\n",
    "\n",
    "image = image.astype(np.int16)\n",
    "image[image == -2000] = 0\n",
    "# plt_img(image)\n",
    "\n",
    "# convert to hu\n",
    "intercept = dicom.RescaleIntercept\n",
    "slope = dicom.RescaleSlope\n",
    "if slope != 1:\n",
    "    image = slope * image.astype(np.float64)\n",
    "    image = image.astype(np.int16)\n",
    "    \n",
    "image += np.int16(intercept)\n",
    "\n",
    "# plt_img(image)\n",
    "plt_img(np.array(image, dtype=np.int16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_img(np.uint8(image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_pickle(OUTPUT_FOLDER + \"ll.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2130</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2131 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     LL\n",
       "0     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "1     [[-2048.0000000000005, -2048.0000000000005, -2...\n",
       "2     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "3     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "4     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "...                                                 ...\n",
       "2126  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2127  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2128  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2129  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2130  [[-2048.0000000000005, -2048.0000000000005, -2...\n",
       "\n",
       "[2131 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2131"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel = pd.read_pickle(OUTPUT_FOLDER + \"data.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "1       [[-1024, -1024, -1024, -1024, -1024, -1024, -1...\n",
       "2       [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "3       [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "4       [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "                              ...                        \n",
       "2126    [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "2127    [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "2128    [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "2129    [[-2048, -2048, -2048, -2048, -2048, -2048, -2...\n",
       "2130    [[-1024, -1024, -1024, -1024, -1024, -1024, -1...\n",
       "Length: 2131, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.12 s, sys: 14.8 s, total: 19.9 s\n",
      "Wall time: 42.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ll1 = pd.read_pickle(OUTPUT_FOLDER + 'wavelet.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ll1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LL</th>\n",
       "      <th>LH</th>\n",
       "      <th>HL</th>\n",
       "      <th>HH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[-4000.000000000001, -4000.000000000001, -400...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2609</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2611</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2612</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2613</th>\n",
       "      <td>[[-4000.000000000001, -4000.000000000001, -400...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1595 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     LL  \\\n",
       "0     [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "1     [[-4000.000000000001, -4000.000000000001, -400...   \n",
       "3     [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "4     [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "5     [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "...                                                 ...   \n",
       "2609  [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "2610  [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "2611  [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "2612  [[-2048.0000000000005, -2048.0000000000005, -2...   \n",
       "2613  [[-4000.000000000001, -4000.000000000001, -400...   \n",
       "\n",
       "                                                     LH  \\\n",
       "0     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "1     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "3     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "4     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "5     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "...                                                 ...   \n",
       "2609  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2610  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2611  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2612  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2613  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "\n",
       "                                                     HL  \\\n",
       "0     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "1     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "3     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "4     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "5     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "...                                                 ...   \n",
       "2609  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2610  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2611  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2612  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "2613  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "\n",
       "                                                     HH  \n",
       "0     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "1     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "3     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "4     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "5     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "...                                                 ...  \n",
       "2609  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "2610  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "2611  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "2612  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "2613  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "\n",
       "[1595 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('testnp', ll1.LL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 189 ms, sys: 342 ms, total: 531 ms\n",
      "Wall time: 531 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "testnp  = np.load(OUTPUT_FOLDER + 'll.npy', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2131"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testnp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'pixel': testnp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>[[-4096.000000000001, -4096.000000000001, -409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2130</th>\n",
       "      <td>[[-2048.0000000000005, -2048.0000000000005, -2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2131 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  pixel\n",
       "0     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "1     [[-2048.0000000000005, -2048.0000000000005, -2...\n",
       "2     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "3     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "4     [[-4096.000000000001, -4096.000000000001, -409...\n",
       "...                                                 ...\n",
       "2126  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2127  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2128  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2129  [[-4096.000000000001, -4096.000000000001, -409...\n",
       "2130  [[-2048.0000000000005, -2048.0000000000005, -2...\n",
       "\n",
       "[2131 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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